Hermetically sealed camera with a graphical processing unit (gpu)

ABSTRACT

The present application is directed to an apparatus including a hermetically sealed housing. The hermetically sealed housing includes an external heat sink integrated with the hermetically sealed housing as a single unit. the external heat sink includes protrusions extending up to an entire length of a surface of the hermetically sealed housing. The apparatus also includes a camera disposed within the hermetically sealed housing. The camera includes a lens and a motor that physically adjusts the lens. The apparatus also includes a graphics processing unit (GPU) coupled to the camera. The GPU is configured to process image information of an image captured by the camera.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/604,852, filed on Jul. 24, 2017, and U.S. Non-Provisional applicationSer. No. 16/042,667 filed Jul. 23, 2018 entitled, “Hermetically SealedCamera with a Graphical Processing Unit (GPU),” the contents of whichare hereby incorporated by reference herein.

BACKGROUND

Machine vision systems include one or more cameras to capture images.The captured images are used in various applications, such as inspectionof items, process control, security, smart city, etc. The imagescaptured by the camera are transmitted to a computer system forprocessing and/or analyzing. In conventional machine vision systems, thecomputer system that receives the images from the camera is separate anddistinct from the camera.

In some conventional machine vision systems, the computer system istypically in proximity to the camera (e.g., in a room near the camera).This can be referred to as edge computing. Typically, the environmentfor machine vision systems introduces a host of factors that cannegatively affect the functionality of the computer system, such asheat, dust, water, bugs, and so on. As a result, there is increasedexpense and burden to protect the computer system from the environment.Moreover, the computer system may fail due to extended exposure to theenvironment.

In other conventional machine vision systems, the computer system is acloud-based computing system where image data is transmitted over anetwork. The image data is then processed using the cloud-basedcomputing system. This typically utilizes a network connection that iscapable of handling a large amount of data to be consistentlytransmitted. However, if the network connection fails (or has decreasedbandwidth or intermittent connection), then then the machine visionsystem may be rendered useless.

Moreover, when a camera is connected over a network, the imageinformation captured by the camera is compressed and subsequentlytransmitted over the network. As a result, the image information is lostor reduced. The image information is dynamic range, temporal and spatialdetail, resolution, etc.

SUMMARY

The following is a summary of the application. The foregoing needs aremet, to a great extent, by the disclosed apparatuses, systems andmethods for a hermetically sealed camera with a graphics processingunit.

An aspect of the application is directed to an apparatus including ahermetically sealed housing. The hermetically sealed housing includes anexternal heat sink integrated with the hermetically sealed housing as asingle unit. the external heat sink includes protrusions extending up toan entire length of a surface of the hermetically sealed housing. Theapparatus also includes a camera disposed within the hermetically sealedhousing. The camera includes a lens and a motor that physically adjuststhe lens. The apparatus also includes a graphics processing unit (GPU)coupled to the camera. The GPU is configured to process imageinformation of an image captured by the camera.

There has thus been outlined, rather broadly, certain embodiments of theapplication in order that the detailed description thereof herein may bebetter understood, and in order that the present contribution to the artmay be better appreciated. There are, of course, additional embodimentsof the application that will be described below and which will form thesubject matter of the claims appended hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of present disclosure are illustrated by way of example, andnot by way of limitation, in the figures of the accompanying drawings inwhich like references indicate similar elements. It should be noted thatdifferent references to “an” or “one” embodiment in this disclosure arenot necessarily to the same embodiment, and such references mean atleast one.

Reference will now be made to the accompanying drawings showing examplesof the present application, and in which:

FIG. 1A is a diagram depicting an isometric view of a camera system, inaccordance with an embodiment of the disclosure;

FIG. 1B is a diagram depicting an exploded isometric view of a camerasystem, in accordance with an embodiment of the disclosure;

FIG. 1C is a diagram depicting an exploded isometric view of a camerasystem, in accordance with an embodiment of the disclosure;

FIG. 1D is a diagram depicting an exploded isometric view of a camerasystem, in accordance with an embodiment of the t disclosure;

FIG. 2 is a diagram depicting a top view of a camera system including aplurality of cameras, in accordance with an embodiment of thedisclosure;

FIG. 3 is a block diagram of a camera system, in accordance with anembodiment of the disclosure;

FIG. 4 is a block diagram of a camera system, in accordance with anembodiment of the disclosure;

FIG. 5 is a flow chart of an embodiment for a method of image processingin a hermetically sealed housing; and

FIG. 6 is a block diagram of a computer system, in accordance with anembodiment of the disclosure.

DETAILED DESCRIPTION

As described above, computer systems that process image data inconventional machine vision systems are separate and distinct from thecameras that capture the images. For example, a camera captures imagesand the images are then transmitted to the computer system. Cameras, inconventional machine systems, do not include a processing unit, such asa graphics processing unit (GPU) or a high performance general purposeprocessing unit). Moreover, processing of image information on acomputer system apart from the camera can negatively affect theconventional machine vision system, as described above.

In contrast to conventional machine vision systems, embodimentsdescribed herein are directed to a camera system that includes a GPU. Inparticular, the camera system (that includes a GPU) is a hermeticallysealed camera system. Hermetically sealed, as used herein, refers tocamera system being air tight and water tight. In other words, thecamera system (within the housing of the camera system) is sealed insuch a way that the camera system is impervious to externalenvironmental influences such as, bug, ambient air, water and so on. Asa result, various components within a housing of the camera system(e.g., camera, processing unit (e.g., GPU), and other electricaldevices) are protected from the external environmental influences.

As such, the images captured by the camera are processed, at least inpart, by the GPU of the camera system (rather than by a separate anddistinct computer system). Additionally, the hermetically sealedstructure of the camera system protects it from the environment (e.g.,water, heat, dust, bugs, etc.). As a result, the camera system is ableto be located and function in harsh environments for an extended amountof time without being negatively affected by the harsh environment.

As described in further detail herein, the camera system can capturelarge amounts of image information (e.g., image data). The camerasystem, located in the field (e.g., a store front, a fish ladder, aremote location), performs data reduction (in real-time) associated withvarious image data collecting applications. Data reduction, as describedherein, is the extraction of high-level information from an informationsource (e.g., a large information source such as streaming video) andthe reduction of the information into specific and concise information.The reduced information can include, but is not limited to,classification, tracking, context extraction and other machine visiontasks.

In one example embodiment, a camera system is disposed underwater totrack fish that swim in proximity to the camera system. In this example,the camera system is able to sense movement of the fish. In response tosensing the movement of a fish, the camera system focuses in on a fishand maintains focus on the fish. While focused on the fish, a species ofthe fish is determined (e.g., using one or more neural networks trainedto determine a fish species). Accordingly, reduced informationassociated with images of the fish (e.g., fish species) is transmittedfrom the camera system.

In another example embodiment, a camera system is disposed proximate toa store front. Image information associated with persons walking by thestore front is captured. The camera system is able to determine reducedinformation associated with the persons walking by the store front(e.g., using one or more neural networks trained to determine number ofpeople, age range of the people). Accordingly, the reduced informationassociated with persons walking by the store front is transmitted fromthe camera system.

In various embodiments, the camera system utilizes machine learning viatraining of a neural network. In general, a neural network is a systemof hardware and/or software patterned after the operation of neurons inthe human brain. Neural networks are adaptive by modifying themselves asthey learn from initial training. Subsequent execution of the neuralnetwork provides more information in determining their output. Theneural network, described herein, is able to recognize patterns of imageinformation from images captured by the camera system. Additionaldescription related to the neural network is provided in further detailbelow.

Embodiments described herein are directed to a camera system thatincludes a hermetically sealed housing, wherein the hermetically sealedhousing includes an external heat sink integrated with the hermeticallysealed housing and a camera disposed within the hermetically sealedhousing. The camera may include a lens and a motor, where the motor isto physically adjust the lens. The camera system also includes a GPUcoupled to the camera and disposed within the hermetically sealedhousing. The GPU can process image information of an image captured bythe camera. Additionally, the external heat sink can absorb heatgenerated by the GPU.

Figures A-D depict embodiments of camera system 100 according to variousembodiments of the disclosure. Figures A-D depict various views of thecamera system. Referring to FIG. 1A (an isometric view of camera system100), camera system 100 includes housing 105 and mount 130. Camerasystem 100, in one embodiment, is coupled to mount 130. Mount 130, invarious embodiments, is attached to a structure. A structure, asdescribed herein, is a physical object that the camera is able to mountto (e.g., tree, wall, rock, etc.). It should be appreciated that camerasystem 100 can be attached to a structure via other mechanical mountingmeans.

Camera system 100 also includes first end cap 125, second end cap 127,front glass 120 and glare cover 110. In various embodiments, second endcap 127 is coupled to a first distal end of housing 105 and first endcap 125 is coupled to a second distal end of the housing 105.Additionally, front glass 120 is also coupled to the second distal endof housing 105. Accordingly, when first end cap 125 and second end cap127 (and front glass 120) are coupled with the respective distal ends ofhousing 105, housing 105 is hermetically sealed (e.g., air tight andwater tight). As will be described in further detail below, with respectto at least FIG. 1B, camera system 100 includes O-rings 126 and 128 andfasteners 140 (e.g., screws) to facilitate in the hermetically sealingof housing 105 (e.g., water, bugs, dust cannot penetrate into thehousing). As depicted, housing 105 is substantially cylindrical.However, it should be appreciated that housing 105 can be any shape thatfacilitates in housing a camera system and being hermetically sealed.

Housing 105 includes heat sink 115. Heat sink 115 is disposed on abottom surface of housing 105. As described in further detail herein,various electrical components (e.g., GPU) are disposed within housing105 in proximity to heat sink 115. Due to housing 105 being hermeticallysealed, heat is unable to be removed from within housing 105 byconventional means (e.g., fan(s), heat exhaust ports, etc.). As such,heat generated by the electrical components within housing 105 isdissipated out of housing 105 to ambient air outside of housing via heatsink 115. That is, camera system 100 is passively cooled (e.g., withoutthe use of a fan within the housing) via heat sink 115.

Heat sink 115 extends a substantial length (e.g., an entire length) ofhousing 105. Heat sink 115 includes various protrusions 116 (e.g., heatsink fins). Protrusions 116 are parallel to one another. Alternatively,protrusions 116 can protrude from heat sink 115 at any orientation tofacilitate dissipating heat from the inside of housing 105 to theoutside of housing 105.

In one embodiment, heat sink 115 is integral with housing 105. That is,heat sink 115 and housing 105 comprise a single unit. Alternatively,heat sink 115 and housing 105 are separate and distinct components thatare coupled together via a fastening means (e.g., screw, glue, etc.).

FIG. 1B is an isometric exploded view of camera system 100 according toan embodiment of the disclosure. FIG. 1B illustrates additionalcomponents of camera system 100. In particular, FIG. 1B illustratesvarious components coupled with housing 105. Camera system 100 includesO-rings 126 and 128 and fasteners 140 (e.g., screws) to facilitate inthe hermetically sealing of housing 105. For example, O-ring 128 isdisposed between second end cap 127 and housing 105 to hermetically sealthe end cap with the housing. O-ring 126 is disposed between first endcap 125 and front glass 120 to facilitate in hermetically sealing thecombination of first end cap 125 and front glass 120 to housing 105.Moreover, fasteners 140-1, 140-2 and 140-3 fasten second end cap 127 tohousing 105. For example, fasteners 140-1, 140-2 and 140-3, whenfastened (e.g., tightened), provide pressure to seat second end cap 127and O-ring 128 to a distal end of housing 105. Likewise, fasteners 140-4and 140-5 fasten at least first end cap 125, front glass 120 and O-ring126 to housing 105. For example, fasteners 140-4 and 145-3, whenfastened (e.g., tightened), provide pressure to seat at least first endcap 125, front glass 120 and O-ring 126 to the opposite distal end ofhousing 105. As a result, housing 105 is hermetically sealed asdescribed herein.

Second end cap 127 includes aperture 129. Aperture 129 is to receive aninput/output (I/O) coupler (not shown). In one embodiment, the I/Ocoupler is a waterproof RJ45 coupler.

FIGS. 1C and 1D are isometric exploded views of camera system 100 thatillustrate various electrical components in the camera system accordingto embodiments of the disclosure. Camera system 100 includes camera 150and system-on-module (SOM) 160. Camera 150 includes lens 152 and motor154. In various embodiments, camera 150 includes a charge-coupled device(CCD) image sensor. Motor 154 is to physically adjust lens 152. Camera150, in various embodiments, includes various lens functions, such as,focus, zoom, aperture and infrared (IR) cut filter. In variousembodiments, motor 154 (or a combination of motors) adjusts the variouslens functions of camera 150 (e.g., focus, zoom, aperture and/or IR cutfilter).

SOM 160 includes various components that control various features of thecamera system. SOM 160 includes GPU 162. GPU 162, in variousembodiments, performs various functions related to processing imageinformation captured by camera 150. Additional description of GPU 162 isdescribed in further detail below. SOM 160 also includes thermaltransfer plate 164. In one embodiment, thermal transfer plate 164transfers heat away from GPU 162 (and any other heat generatingcomponents of the camera system). Thermal transfer plate 164 is placedin proximity to heat sink 115 of housing 105. As such, heat generated byGPU 162 is transferred away from the GPU by thermal transfer plate 164and heat sink 115. Further description of SOM 160 is described infurther detail below with respect to at least FIG. 3.

As depicted in at least Figures A-D, camera system 100 includes a singlecamera 150 that is disposed proximate to the second distal end ofhousing 105. Alternatively, camera system 100 can include a secondcamera (similar to camera 150) that is disposed at the first distal endof housing 105. Accordingly, camera system 100 includes two cameras thatcapture images from opposite ends of housing 105.

As described in further detail below, reduced image information isgenerated, at least in part, by GPU 162. The reduced image informationis then transmitted over a network (e.g., local area network (LAN), widearea network (WAN), etc.). The reduced information can be transmitted atvarious instances, such as in real-time, periodically, and so on. In oneembodiment, reduced information is stored in storage and/or memory atthe camera system 100.

FIG. 2 depicts a top view of camera system 200 according to anembodiment of the disclosure. Camera system 200 is similar to camerasystem 100 depicted with respect to FIG. 1. As shown in FIG. 2, system200 includes six cameras 220 (e.g., cameras 220-1 through 220-6)disposed in housing 210. Cameras 220 are positioned radially withrespect to one another. The form-factor of housing 210 can be any shapethat facilitates in housing a plurality of cameras. It should beappreciated that camera system 200 can include any number of cameras(e.g., 2 cameras, 3 cameras, 4 cameras, 5 cameras, etc.) and is notlimited to the number of cameras depicted in FIG. 2. FIG. 3 illustratesa block diagram of camera system 300 (e.g., camera system 100 and camerasystem 200) according to an embodiment of the disclosure. Camera system300, in various embodiments, includes various modular components (e.g.,circuitry) coupled to carrier board 305 (e.g., PCB board). A carrierboard, as described herein, is the junction between SOM 310 and variousmodules (or circuitry) that are coupled to carrier board 305. Themodules that are coupled to the carrier board are described in furtherdetail herein. The modular camera system allows for various combinationsof functionality which is described in further detail below.

Carrier board 305 includes SOM 310. In one embodiment, SOM 310 includesGPU 312 and application-specific integrated circuit (ASIC) 314. Asdescribed herein, GPU 312 is communicatively coupled to camera 350 (e.g.camera 150) within housing 105. As a result, uncompressed imageinformation (e.g., image data) captured by camera 350 is processed byGPU 312. For example, image information may be captured in a high bitdepth (e.g., >8 bits such as 10 bits, 12 bits and so on) and sent to GPU312 for processing in the high bit depth. Accordingly, image informationcaptured by the camera such as dynamic range, temporal and spatialdetail, and resolution is not lost due to compression.

In a more specific example, the high bit depth of the image informationprovided to the GPU is beneficial in facial recognition. The high bitdepth (e.g., 10 bit or 12 bits) provides an increased number of discreteintervals of pixel information to the GPU to enhance the process offacial recognition. In such an example, 12 bit image informationincludes 4096 levels of color and luminance. In a convention camerasystem, 8 bit image information provided to a processing unit (e.g., CPUor GPU). The 8 bit image information includes 256 levels of color andluminance information.

Additionally, a GPU has increased image processing functionality ascompared to a conventional CPU. As such, the GPU is able to processframes at a faster rate than a conventional CPU. This results in the GPUable to process increased information in the time domain (that was lostdue to compression of the image information in the conventional system).Also, in the facial recognition example, spatial information is retainedby doing an object detection followed by a bounding box subsample of theobject/subject of interest.

GPU 312, in various embodiments, is able to generate the reducedinformation, as described above. For example, the camera system capturesimage information (e.g., image data). The GPU is then able to reduce thecaptured data into specific and concise information. For example, in thefish ladder use case, the camera system can capture hundreds orthousands of images of fish swimming by the camera system. The GPU 312is then able to analyze the data and generate reduced informationassociated with the fish swimming by the camera system. The reducedinformation can include, but is not limited to, the quantity of fish,the type of species, time-stamp of the fish passing by the camera systemor a combination thereof. For example, the reduced information isgenerated by GPU 312 performing machine learning (e.g., neural networksand deep neural networks (DNN)), as described herein.

ASIC 314, in various embodiments, provides image pre-processing for GPU312. It should be appreciated that image pre-processing, as describedherein, is a functionality (or “instinct”) of camera system 300 that issimilar in function to the human visual cortex. As such, the imagepre-processing enables the camera system to “sense” certain subjects(e.g., people and faces), movement (e.g., optical flow), colorinformation, and fusion of multiple types of image sensors (e.g.,infrared sensors). In various embodiments, the pre-processinginformation is provided to the GPU via the same channels as the originalpicture information. As a result, the camera system is providedadditional (pre-processed) sensory information from which to makedecisions to identify, detect and/or locate objects.

ASIC 314, in one embodiment, is a field-programmable gate array (FPGA).In one embodiment, ASIC 314 stitches together feeds from a plurality ofcameras. A camera feed is the output of the camera 150 (e.g., video,images). The camera feed can be image information generated inreal-time. For example, referring to FIG. 2, six different cameras(e.g., cameras 220-1 through 220-6) can capture separate images at thesame time. The ASIC can stitch the six images together to generate asingle image (or camera feed). In such an example, the single image (orcamera feed) is six separate images (or camera feeds) combined togetherto appear as a single image (e.g., a 360-degree panoramic image). Thestitched image is then transmitted to the GPU for further processing.

In another embodiment, the pre-processing of image data by the ASIC 314includes, but is not limited to, identifying an object in an image(e.g., fish species, child, adult, etc.), location of objects in theimage (e.g., location of faces, fish, etc.), movement in the image(e.g., fish swimming, pedestrians walking by store front, etc.),detection of the object (e.g., determine that a fish is in the image),etc. The pre-processed information is then transmitted to the GPU forfurther processing.

Carrier board 305 includes various modules (or circuitry) that arecoupled to carrier board 305. The modules/circuitry, in one embodiment,are printed circuit boards (PCBs) that releasably connect with thecarrier board. This enables for quick swapping of various modules toprovide for various functionality of the camera system. In someembodiments, carrier board 305 includes, but is not limited to, wiredcircuitry 320, wireless circuitry 322, I/O circuitry 330, globalpositioning circuitry (GPS) 332, lens driver circuitry 334, audio inputcircuitry 335, and satellite communication circuitry 336.

Wired circuitry 320 allows for wired communication from the camerasystem. The wired communication can be communicatively coupled to adevice of an end user. Wireless communication circuitry 322 allows forwireless communication over a network. For example, the wirelesscommunication can be, but is not limited to, Bluetooth, Wi-Fi, etc.Wired circuitry and/or wireless circuitry can implement variousprocessing protocols such as Modbus, controller area network (CAN bus),Ethernet/IP, etc. In various embodiments, communication to/from thecamera system can includes satellite communication.

I/O circuitry 330 allows for various types of I/O protocols/standardssuch as, but not limited to, USB 2.0, USB 3.0, RJ 45, etc. GPS circuitry332 allows for satellite-based radio-navigation with the camera system.For example, image data processed by the camera system is associatedwith GPS coordinates. Lens driver circuitry 334 drives one or motors ofthe camera to change various functions of the lens of the camera (e.g.,focus, zoom, aperture and IR cut filter, etc.). Audio input circuitry335 allows for audio input at the location of the camera system. Forexample, audio input circuitry 335 is a microphone that captures audiosignals (e.g., people talking) at the location of the camera system.Satellite communication circuitry 336 enables communication via asatellite. For example, the data from the camera system is transmittedto a satellite via satellite communication circuitry 336.

In various embodiments, camera system 300 includes othermodules/circuitry (not shown). For example, camera system 300 includespower-over-Ethernet (POE) (e.g., up to 90-watt power converter) thatallows the camera system to be powered from an Ethernet cable (and notfrom a separate power supply). Additionally, camera system 300 caninclude a power conversion circuitry. The power input can be in a rangeof 60 volt (V) to 12V or include other voltages such as 5V, 4V, 3.3V,2.8V, 1.8V and 1.2V. In some embodiments, camera system 300 includesmodules/circuitry such as gigabit Ethernet, SD Card, M.2 PCIe, fan, USB,and general I/O.

In various embodiments, the camera system implements machine learning.In particular, GPU 312 is able to recognize various patterns in imagedata via processing of one or more neural networks. For example, theneural network is trained to facilitate in the machine learning of thecamera system. In one embodiment, the neural network is trained torecognize patterns in image data generated by the camera system. Forexample, in the fish ladder use case, the neural network is trained tolook for fish in the image data and determine, among other things, aquantity and/or species of fish. Accordingly, the neural network modelis trained to automatically determine a quantity and/or species of fishbased on image information captured by the camera of the camera system.In one embodiment, the neural network is trained based on receivingimage information of various fish that includes the respective speciesof fish and also based on feedback of the received fish speciesdeterminations made by the neural network model. In various embodiments,the GPU is programmed using pre-developed frameworks for splittingneural networks into kernels or functional pieces that are processed inparallel in the GPU. As a result, the GPU is well-suited for inferencefrom the convolution or deep neural networks.

In one embodiment, carrier board 305 is communicatively coupled tocamera 350. In one embodiment, camera system 300 includes a singlecamera (e.g., camera 154) that is coupled to carrier board 305.Alternatively, camera system 300 includes a plurality of cameras (e.g.,cameras 210-1 through 210-6) that are coupled to carrier board 305.

As described above, camera system 300, includes various modularcomponents (e.g., circuitry) coupled to carrier board 305 (e.g., PCBboard). The modular camera system allows for various combinations offunctionality which is described in further detail below. For example,if it is desired that camera system 300 utilizes wireless communicationwith a network, then the camera system includes wireless circuitry 322(and not wired circuitry 320) coupled to the carrier board. Similarly,if it is desired that camera system 300 utilizes a wired communicationprotocol to output image information, then camera system includes wiredcircuitry 320 (and not wireless circuitry) that is releasably coupled tothe carrier board.

FIG. 4 illustrates a block diagram of camera system 400 (e.g., camerasystem 100 and camera system 200) according to an embodiment of thedisclosure. Camera system 400, in various embodiments, includes variousmodular components (e.g., circuitry) coupled to carrier board 405 (e.g.,PCB board). The modular camera system allows for various combinations offunctionality which is described in further detail below. For example,as described in further detail herein, the modular components (e.g.,circuitry) are electrical modular subsystems that are disposed onfunctionally distinct circuits. The modular components can include, butare not limited to, a satellite modem, 4G Long-Term Evolution (LTE)modem, lighting module, I/O module. In various embodiments, the cameraportion of the modules includes a system of pluggable camera input andlens controls. This allows the camera system to accept camera feeds(e.g., 1-6 camera feeds) and control lenses (e.g., 1-6 camera lenses)depending on the desired configuration.

Camera system 400 includes power circuitry 410 and SOM 415. In variousembodiments, power circuitry 410 and SOM 415 are releasably coupled tocarrier board 405. Power circuitry 410 receives Power over Ethernet(POE) 402. POE 402 is to power camera system 400, such as SOM 415 andother components as described herein. Power circuitry 410 includes powerconverter circuitry 412. Power converter circuitry 412 is to convert POE402 to the desired power for the respective components in camera system400 (e.g., SOM 415). In various embodiments, power supply circuitry 410includes other components/circuitry such as, but not limited to, storage(e.g., secure digital (SD) card) and features for connection,communication and/or power supply (e.g., M.2 slot, USB 2.0/3.0, and CANbus)

SOM 415 includes communication circuitry 417 and GPU 418. Communicationcircuitry 417 enables communication via various communication protocols(e.g., Bluetooth, WIFI, etc.).

Camera system 400 also includes single camera circuitry 420, 3-cameracircuitry 430. 6-camera circuitry 440 and 6-camera circuitry 450. Invarious embodiments, single camera circuitry 420, 3-camera circuitry430, 6-camera circuitry 440 and 6-camera circuitry 450 are releasablycoupled to carrier board 405.

Single camera circuitry 420 is circuitry that enables camera system 400to implement a single camera (e.g., camera 154 in system 100). Singlecamera circuitry 420 includes, among other things, lens driver 422 andcamera serial interface (CSI) 424. Lens driver circuitry 422 drives oneor motors of the camera to change various functions of the lens of thecamera (e.g., focus, zoom, aperture and IR cut filter, etc.). CSI 424 isthe interface between the camera and the host processor (e.g., GPU). Ingeneral, CSI is a specification of the Mobile Industry ProcessorInterface (MIPI) Alliance that defines the interface between the cameraand the host processor. It should be appreciated that when camera system400 is implemented with a single camera then single camera circuitry 420is releasably coupled with carrier board 405 (and SOM 415).

3-camera circuitry 430 enables camera system 400 to implement threecameras. 3-camera circuitry 430 includes at least CSI 434. CSI 434 issimilar to CSI 424 described above. It should be appreciated that whencamera system 400 is implemented with three cameras then 3-cameracircuitry 430 is releasable coupled with carrier board 405 (and SOM415).

6-camera circuitry 440 enables camera system 400 to concurrentlyimplement six different cameras. In various embodiments, 6-cameracircuitry 440 includes CSI 444. CSI 444 is similar to CSI 424 (and CSI434) described above. It should be appreciated that when camera system400 is implemented with six different cameras then, in one embodiment,6-camera circuitry 444 420 is releasable coupled with carrier board 405(and SOM 415).

6-camera circuitry 450 enables camera system 400 to concurrentlyimplement six different cameras. In one embodiment, 6-camera circuitry440 is separate and distinct from 6-camera circuitry 440. Alternatively,the features and functionality of 6-camera circuitry 440 and 450 arecombined to form single 6-camera circuitry. 6-camera circuitry 450includes FPGA 452 (or ASIC). FPGA 452 is similar to FPGA 314, asdescribed above. For example, FPGA 452 provides image pre-processing toGPU 418 and image stitching, as described above. The imagepre-processing can include dewarp, optical flow and stereo imageprocessing. For example, the image pre-processing enables the camerasystem to “sense” certain subjects (e.g., people and faces), movement(e.g., optical flow), color information, and fusion of multiple types ofimage sensors (e.g., infrared sensors). In various embodiments, thepre-processing information is provided to the GPU via the same channelsas the original picture information. As a result, the camera system isprovided additional (pre-processed) sensory information from which tomake decisions to identify, detect and/or locate objects

Camera system 400 also includes communication circuitry 460, satellitecommunication circuitry 470, lighting circuitry 480 and serial I/Ocircuitry 490. Communication circuitry 460 supports variouscommunication means for camera system 400. In one embodiment,communication circuitry 460 includes a cellular modem to enable camerasystem 400 to perform cellular communication. In another embodiment,camera system includes a GPS modem to enable camera system 400 tocommunicate with GPS satellites and calculate a location of the camerasystem.

Satellite communication circuitry 470 enables camera system 400 toperform satellite communication. For example, camera system 400transmits image data (e.g., reduced information) via satellitecommunication protocols.

Lighting circuitry 480 enables control of lighting functionality ofcamera system 400. For example, camera system includes lights (e.g.,light emitting diodes (LED)) to illuminate objects in proximity to thecamera.

Serial I/O circuitry 490 enables camera system 400 to perform serialcommunication. Serial I/O circuitry 490 supports various serialcommunication protocols and I/O modules such as CAN bus, RS485, RS 232solid state relay (SSR), etc.

FIG. 5 depicts a flow chart of method 500 related to processing imageinformation according to an embodiment of the disclosure. The methodsand each of their individual functions, routines, subroutines, oroperations may be performed by processing logic of a computing device(e.g., GPU) executing the method. The processing logic may includehardware, software, firmware, or a combination thereof. For example,processing logic may include a general purpose and/or special purposeprocessor that executes instructions for performing the methods.

Referring to FIG. 5, at 510 of method 500, a camera disposed in ahermetically sealed housing captures an image. For example, camera 150is disposed in hermetically sealed housing 105. Camera system 100 (thatincludes camera 150), in one embodiment, is disposed underwater andtakes images of fish in proximity to the camera. In various embodiments,hermetically sealed housing 105 comprises an external heat sink 115integrated with the hermetically sealed housing.

At 520, a GPU processes image information of the image to recognizepatterns in the image information. For example, camera system 100 isdisposed under water to track fish swimming past the camera system. Inone embodiment, GPU 312 processes a neural network that is traineddetermine a species of fish by its visual appearance. For example, theneural network is trained to determine the visual distinctions betweenvarious species of fish.

In various embodiments, camera system 300 (that includes GPU 312) isdisposed in a hermetically sealed housing (e.g., housing 105).Additionally, the housing includes an external heat sink 115 to absorbheat generated by the GPU and passively cool the camera system.

At 530, the processed information is output from the camera system. Forexample, camera system 100 is communicatively coupled to a network(e.g., LAN, WAN, etc.). As such, the reduced information (e.g., quantityof fish, species of fish, quantity of pedestrians walking by a storefront) is transmitted from the camera system over the network.

FIG. 6 depicts a block diagram of a computer system operating accordingto an embodiment of the disclosure. In various illustrative examples,computer system 600 may correspond to camera system 100 of FIG. 1,camera system 200 of FIG. 2, camera system 300 of FIG. 3, and/or camerasystem 400 of FIG. 4.

In certain implementations, computer system 600 may be connected (e.g.,via a network, such as a Local Area Network (LAN), an intranet, anextranet, or the Internet) to other computer systems. Computer system600 may operate in the capacity of a server or a client computer in aclient-server environment, or as a peer computer in a peer-to-peer ordistributed network environment. Computer system 600 may be provided bya personal computer (PC), a tablet PC, a set-top box (STB), a PersonalDigital Assistant (PDA), a cellular telephone, a web appliance, a laptopcomputer, a tablet computer, a server computing device, a networkrouter, switch or bridge, an electronic display device, or any devicecapable of executing a set of instructions (sequential or otherwise)that specify actions to be taken by that device. Further, the term“computer” shall include any collection of computers that individuallyor jointly execute a set (or multiple sets) of instructions to performany one or more of the methods described herein.

In a further aspect, the computer system 600 may include a processingdevice 602, a main (volatile) memory 604 (e.g., random access memory(RAM)), a static (non-volatile) memory 606 (e.g., read-only memory (ROM)or electrically-erasable programmable ROM (EEPROM)), and/or a datastorage device 618, which may communicate with each other via a bus 608.

Processing device 602 may be provided by one or more processing devicessuch as a general purpose processor (such as, for example, a complexinstruction set computing (CISC) microprocessor, a reduced instructionset computing (RISC) microprocessor, a very long instruction word (VLIW)microprocessor, a microprocessor implementing other types of instructionsets, or a microprocessor implementing a combination of types ofinstruction sets) or a specialized processor (such as, for example, anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), or a networkprocessor).

Computer system 600 may further include a network interface device 622(e.g., a wireless communication module, wireless modem, etc.). Computersystem 600 also may include a video display unit 610 (e.g., an LCD), aninput device 612 (e.g., a keyboard, touch screen, touchpad, etc.), and acursor control device 614 (e.g., a mouse).

Data storage device 618 may include a non-transitory computer-readablestorage medium 624 on which it may store instructions 626 encoding anyone or more of the methods or functions described herein, includinginstructions encoding method, 500 for image information processing. Forexample, data storage device 618 may include instructions 626 for imageinformation processing 692, which may correspond to similarly namedcomponents described earlier herein.

Instructions 626 may also reside, completely or partially, within mainmemory 604 and/or within processing device 602 during execution thereofby computer system 600, hence, main memory 604 and processing device 602may also constitute machine-readable storage media.

While computer-readable storage medium 624 is shown in the illustrativeexamples as a single medium, the term “computer-readable storage medium”shall include a single medium or multiple media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storethe one or more sets of executable instructions. The term“computer-readable storage medium” shall also include any non-transitorytangible medium that is capable of storing or encoding a set ofinstructions for execution by a computer or device that cause thecomputer or device to perform any one or more of the methods describedherein. The term “computer-readable storage medium” shall include, butnot be limited to, solid-state memories, optical media, and magneticmedia.

The methods, components, and features described herein may beimplemented by discrete hardware components or may be integrated in thefunctionality of other hardware components such as ASICS, FPGAs, DSPs orsimilar devices. In addition, the methods, components, and features maybe implemented by firmware modules or functional circuitry withinhardware devices. Further, the methods, components, and features may beimplemented in any combination of hardware devices and softwarecomponents, or only in software.

Unless specifically stated otherwise, terms such as “receiving”,“identifying”, “determining”, “transmitting”, “capturing”, or the like,refer to actions and processes performed or implemented by a computersystem that manipulates and transforms data represented as physical(electronic) quantities within the computer system registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage or display devices. Also, the terms “first,”“second,” “third,” “fourth,” etc. as used herein are meant as labels todistinguish among different elements and may not necessarily have anordinal meaning according to their numerical designation.

Examples described herein also relate to an apparatus for performing themethods described herein. This apparatus may be specially constructedfor performing the methods described herein, or it may include a generalpurpose computer system selectively programmed by a computer programstored in the computer system. Such a computer program may be stored ina computer-readable tangible storage medium.

The methods and illustrative examples described herein are notinherently related to any particular computer or other apparatus.Various general purpose systems may be used in accordance with theteachings described herein, or it may prove convenient to construct morespecialized apparatus to perform method 500 and/or each of itsindividual functions, routines, subroutines, or operations. Examples ofthe structure for a variety of these systems are set forth in thedescription above.

The above description is intended to be illustrative, and notrestrictive. Although the present disclosure has been described withreferences to specific illustrative examples and implementations, itwill be recognized that the present disclosure is not limited to theexamples and implementations described. The scope of the disclosureshould be determined with reference to the following claims, along withthe full scope of equivalents to which the claims are entitled.

What is claimed is:
 1. An apparatus comprising: a hermetically sealedhousing including an external heat sink integrated with the hermeticallysealed housing as a single unit, the external heat sink includingprotrusions extending up to an entire length of a surface of thehermetically sealed housing; a camera disposed within the hermeticallysealed housing, the camera including a lens and a motor that physicallyadjusts the lens; and a graphics processing unit (GPU) coupled to thecamera and configured to process image information of an image capturedby the camera.
 2. The apparatus of claim 1, wherein the GPU is locatedwithin the hermetically sealed housing, and the GPU is disposed adjacentto the external heat sink.
 3. The apparatus of claim 2, wherein the GPUis configured to apply a neural network to recognize patterns of theimage information.
 4. The apparatus of claim 2, wherein the externalheat sink absorbs heat generated by the GPU.
 5. The apparatus of claim1, further comprising: an application-specific integrated circuit (ASIC)coupled to the GPU, wherein the ASIC pre-processes the image informationof the image captured by the camera.
 6. The apparatus of claim 5,wherein the ASIC pre-processes the image information based on one ormore of movement of an object, detection of the object, identificationof the object, optical flow, and stereo image processing.
 7. Theapparatus of claim 5, wherein the GPU and the ASIC are part of asystem-on module (SOM).
 8. The apparatus of claim 7, wherein the SOMfurther comprises one or more of: a communication circuit; aninput/output (I/O) circuit; a lens driver circuit; a global positioningsystem (GPS) circuit; an audio input circuit; and a satellitecommunication circuit.
 9. The apparatus of claim 1, further comprising:another camera disposed within the hermetically sealed housing, whereinthe GPU processes image information captured by the other camera.
 10. Acamera system comprising: a hermetically sealed housing comprising anexternal heat sink integrated therewith as a single unit, the externalheat sink extending an entire length of a surface of the hermeticallysealed housing; a plurality of cameras disposed within the hermeticallysealed housing; and a system-on-module (SOM) coupled to the plurality ofcameras, the SOM including an application-specific integrated circuit(ASIC) configured to pre-process image information captured by one ormore of the plurality of cameras.
 11. The camera system of claim 10,wherein the SOM further comprises a graphics processing unit (GPU)configured to process the pre-process image information.
 12. The camerasystem of claim 11, wherein the GPU is configured to apply a neuralnetwork to recognize patterns of the image information.
 13. The camerasystem of claim 10, wherein the ASIC pre-processes the image informationbased on one or more of movement of an object, detection of the object,identification of the object, optical flow, and stereo image processing.14. The camera system of claim 10, wherein the SOM further comprises oneor more of: a wireless communication circuit releasably coupled to theSOM; a wired communication circuit releasably coupled to the SOM; aninput/output (I/O) circuit releasably coupled to the SOM; a lens drivercircuit releasably coupled to the SOM; a global positioning system (GPS)circuit releasably coupled to the SOM; an audio input circuit releasablycoupled to the SOM; and a satellite communication circuit releasablycoupled to the SOM.
 15. The camera system of claim 10, wherein each ofthe plurality of cameras comprises a motor configured to adjust acharacteristic of a respective lens, the characteristic including one ormore of focus, zoom, aperture and infrared cut filter.
 16. The camerasystem of claim 11, wherein the GPU is disposed adjacent to the externalheat sink.
 17. The camera system of claim 10, wherein the hermeticallysealed housing is substantially cylindrical.
 18. A method for processingimage information comprising: capturing an image via a camera disposedin a hermetically sealed housing, the hermetically sealed housingincluding an external heat sink integrated therewith and extending anentire length of a surface of the hermetically sealed housing; andprocessing the captured image via a graphics processing unit (GPU)disposed in the hermetically sealed housing; and outputting theprocessed, captured image.
 19. The method of claim 18, wherein theprocessing step includes: recognizing, via a neural network, patterns ininformation of the captured image.
 20. The method of claim 18, whereinthe outputting step includes wirelessly transmitting, in real-time overa network, the processed image information via communications circuitrydisposed in the hermetically sealed housing.